https://youtu.be/pjYxtJ0pTRY

This project is based on the Nvidia open source project "jetson-reinforcement" developed by Dustin Franklin. The goal of the project is to create a DQN agent and define reward functions to teach a robotic arm to carry out two primary objectives:

1. Have any part of the robot arm touch the object of interest, with at least a 90% accuracy.
2. Have only the gripper base of the robot arm touch the object, with at least a 80% accuracy.

Run the following commands from terminal to build the project from source:

$ sudo apt-get install cmake
$ git clone http://github.com/udacity/RoboND-DeepRL-Project
$ cd RoboND-DeepRL-Project
$ git submodule update --init
$ mkdir build
$ cd build
$ cmake ../
$ make

During the cmake step, Torch will be installed so it can take awhile. It will download packages and ask you for your sudo password during the install.

To make sure that the reinforcement learners are still functioning properly from C++, a simple example of using the API called catch is provided. Similar in concept to pong, a ball drops from the top of the screen which the agent must catch before the ball reaches the bottom of the screen, by moving it's paddle left or right.

To test the textual catch sample, run the following executable from the terminal:

$ cd RoboND-DeepRL-Project/build/aarch64/bin
$ ./catch 
[deepRL]  input_width:    64
[deepRL]  input_height:   64
[deepRL]  input_channels: 1
[deepRL]  num_actions:    3
[deepRL]  optimizer:      RMSprop
[deepRL]  learning rate:  0.01
[deepRL]  replay_memory:  10000
[deepRL]  batch_size:     32
[deepRL]  gamma:          0.9
[deepRL]  epsilon_start:  0.9
[deepRL]  epsilon_end:    0.05
[deepRL]  epsilon_decay:  200.0
[deepRL]  allow_random:   1
[deepRL]  debug_mode:     0
[deepRL]  creating DQN model instance
[deepRL]  DQN model instance created
[deepRL]  DQN script done init
[cuda]  cudaAllocMapped 16384 bytes, CPU 0x1020a800000 GPU 0x1020a800000
[deepRL]  pyTorch THCState  0x0318D490
[deepRL]  nn.Conv2d() output size = 800
WON! episode 1
001 for 001  (1.0000)  
......
WON! episode 112
080 for 112  (0.7143)  20 of last 20  (1.00)  (max=1.00)

Internally, catch is using the dqnAgent API from our C++ library to implement the learning.

To get started with the project environment, run the following:

$ cd RoboND-DeepRL-Project/build/aarch64/bin
$ chmod +x gazebo-arm.sh
$ ./gazebo-arm.sh

The plugins which hook the learning into the simulation are located in the gazebo/ directory of the repo. The RL agent and the reward functions are to be defined in ArmPlugin.cpp.

• Task 1

• Task 2